# Copyright (c) 2024 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import numpy as np
import paddle
from experimental.observer.channel_wise import ChannelWiseObserver
from paddle.quantization.factory import ObserverFactory


class AbsMaxHeadwiseObserver(ObserverFactory):
    r"""
    It collects channel-wise maximum absolute values of target weights.
    Args:
        bit_length(int, optional): Number of bits to represent an quantized integer in binary.
        dtype(str, optional): The data type of input tensor.
        name (str, optional): This parameter is used by developers to print debugging information. \
            For details, please refer to :ref:`api_guide_Name`. Default is None.
    Examples:
       .. code-block:: python
            from paddle.quantization import QuantConfig
            from paddle.quantization.quanters import AbsMaxHeadwiseObserver
            quanter = AbsMaxHeadwiseObserver()
            q_config = QuantConfig(activation=None, weight=quanter)
    """

    def __init__(self, quant_bits=8, quant_axis=None):
        super(AbsMaxHeadwiseObserver, self).__init__(quant_bits=quant_bits, quant_axis=quant_axis)

    def _get_class(self):
        return AbsMaxHeadwiseObserverLayer


class AbsMaxHeadwiseObserverLayer(ChannelWiseObserver):
    def __init__(self, layer, quant_bits=8, quant_axis=None):
        super(AbsMaxHeadwiseObserverLayer, self).__init__(
            layer, quant_bits=quant_bits, sign=True, symmetric=True, quant_axis=quant_axis
        )
        self.quant_bits = quant_bits
        self.calibration_loss = float("inf")
        self.qmin, self.qmax = self.qmin_qmax
        self._layer = layer
        self._max = None
        self._scale = None
        self._zero_point = None

    def forward(self, inputs):
        self._max = self._cal_abs_max(inputs)
        return inputs

    def _cal_abs_max(self, inputs):
        reduce_axis = tuple([i for i in range(len(inputs.shape)) if i != self.quant_axis()])
        abs_max_values = paddle.max(paddle.abs(inputs), axis=reduce_axis).cast("float32")
        abs_max_values = paddle.where(abs_max_values == np.float32(0.0), np.float32(1e-8), abs_max_values)

        if self._max is not None:
            abs_max_values = paddle.maximum(abs_max_values, self._max)

        return abs_max_values

    def min_value(self) -> float:
        return 0.0

    def max_value(self) -> float:
        return self._max

    def cal_thresholds(self):
        """Compute thresholds for MAX function."""
        self._scale = self._max
        self._zero_point = paddle.zeros_like(self._scale)

    def scales(self):
        """Return output scales."""
        if self._scale is None:
            self.cal_thresholds()
        return self._scale

    def zero_points(self):
        """Return output zero points."""
        if self._zero_point is None:
            self.cal_thresholds()
        return self._zero_point
